Prefiltrado

Se realiza un prefiltrado de los genes con muy poca expresión. Se utiliza como umbral de prefiltrado, 58 counts por gen y muestra.

## [1] "Número inicial de genes: 19398"
## [1] "Número de genes despreciados en el pre-filtrado: 5706"
## [1] "Número de genes que pasan el prefiltrado: 13692"

Normalización

Se normaliza la matriz de counts mediante normalización TPM con la función count2tpm() del paquete IOBR.

## [1] "Número de genes omitidos durante la normalización TPM, debido a que su longitud no está disponible en Biomart: 514"

Deconvolución

## [1] TRUE
## [1] TRUE
## [1] TRUE

Abundancia Celular por Poblaciones

Resultados de la deconvolución con EPIC

Resultados de la deconvolución con quanTIseq

Resultados de la deconvolución con xCell

Resultados de la deconvolución con CIBERSORTx

Dado que mediante el paquete immunedeconv, no es posible ejecutar la deconvoloución del TME con CIBERSORTx, utilizando el código fuente original. Se ejecuta la deconvolución en el servidor web https://cibersortx.stanford.edu/. Una vez realizada la deconvolución se descarga la matriz de resultados y se importa a R. Esta matriz de resultados, contiene los valores absolutos de cada fracción celular por muestra y el valor total (sumatorio) en su última columna. Para obtener cada fracción celular (valor relativo respecto al total), se procede a crear una nueva matriz con cada ratio.

## [1] TRUE

Composición celular del TME

Composición celular del TME por Muestra

Composición celular del TME por Grupo

Contrastes de Hipótesis

Normalidad & Homocedasticidad

Se comprueba la normalidad de las fracciones por grupo y tipo celular y la homocedasticidad entre los grupos PCNSL vs SCNSL y DLBCL with CNS inv vs DLBCL wo CNS inv, para cada tipo celular. La conclusión es que para algunos tipos celulares la distribución de las fracciones celulares de cada grupo, no siguen una normal. (Se ha comprobado los resultados de EPIC).

## # A tibble: 40 × 4
## # Groups:   TipoCelular [8]
##    TipoCelular                  Group                     shapiro_test   p.value
##    <chr>                        <fct>                     <list>           <dbl>
##  1 B cell                       DLBCL with CNS inv        <htest>      0.422    
##  2 B cell                       DLBCL wo CNS inv          <htest>      0.0830   
##  3 B cell                       PCNSL                     <htest>      0.873    
##  4 B cell                       REACTIVE BRAIN INFILTRATE <htest>      0.00435  
##  5 B cell                       SCNSL                     <htest>      0.853    
##  6 Cancer associated fibroblast DLBCL with CNS inv        <htest>      0.0291   
##  7 Cancer associated fibroblast DLBCL wo CNS inv          <htest>      0.00450  
##  8 Cancer associated fibroblast PCNSL                     <htest>      0.0000589
##  9 Cancer associated fibroblast REACTIVE BRAIN INFILTRATE <htest>      0.516    
## 10 Cancer associated fibroblast SCNSL                     <htest>      0.0248   
## # ℹ 30 more rows
## $`B cell`
## $`B cell`[[1]]
## $`B cell`[[1]]$grupo
## [1] "PCNSL" "SCNSL"
## 
## $`B cell`[[1]]$normalidad
## $`B cell`[[1]]$normalidad[[1]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.97518, p-value = 0.873
## 
## 
## $`B cell`[[1]]$normalidad[[2]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.96663, p-value = 0.8532
## 
## 
## 
## $`B cell`[[1]]$homocedasticidad
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value  Pr(>F)  
## group  1  3.7616 0.06537 .
##       22                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`B cell`[[2]]
## $`B cell`[[2]]$grupo
## [1] "DLBCL with CNS inv" "DLBCL wo CNS inv"  
## 
## $`B cell`[[2]]$normalidad
## $`B cell`[[2]]$normalidad[[1]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.92726, p-value = 0.4215
## 
## 
## $`B cell`[[2]]$normalidad[[2]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.87229, p-value = 0.08295
## 
## 
## 
## $`B cell`[[2]]$homocedasticidad
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.0368 0.8499
##       19               
## 
## 
## 
## $`Cancer associated fibroblast`
## $`Cancer associated fibroblast`[[1]]
## $`Cancer associated fibroblast`[[1]]$grupo
## [1] "PCNSL" "SCNSL"
## 
## $`Cancer associated fibroblast`[[1]]$normalidad
## $`Cancer associated fibroblast`[[1]]$normalidad[[1]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.70209, p-value = 5.888e-05
## 
## 
## $`Cancer associated fibroblast`[[1]]$normalidad[[2]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.74154, p-value = 0.02481
## 
## 
## 
## $`Cancer associated fibroblast`[[1]]$homocedasticidad
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.8769 0.3592
##       22               
## 
## 
## $`Cancer associated fibroblast`[[2]]
## $`Cancer associated fibroblast`[[2]]$grupo
## [1] "DLBCL with CNS inv" "DLBCL wo CNS inv"  
## 
## $`Cancer associated fibroblast`[[2]]$normalidad
## $`Cancer associated fibroblast`[[2]]$normalidad[[1]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.82502, p-value = 0.02914
## 
## 
## $`Cancer associated fibroblast`[[2]]$normalidad[[2]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.77565, p-value = 0.004502
## 
## 
## 
## $`Cancer associated fibroblast`[[2]]$homocedasticidad
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  2.5778 0.1249
##       19               
## 
## 
## 
## $`T cell CD4+`
## $`T cell CD4+`[[1]]
## $`T cell CD4+`[[1]]$grupo
## [1] "PCNSL" "SCNSL"
## 
## $`T cell CD4+`[[1]]$normalidad
## $`T cell CD4+`[[1]]$normalidad[[1]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.66304, p-value = 2.082e-05
## 
## 
## $`T cell CD4+`[[1]]$normalidad[[2]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.55437, p-value = 0.0001413
## 
## 
## 
## $`T cell CD4+`[[1]]$homocedasticidad
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.6096 0.4433
##       22               
## 
## 
## $`T cell CD4+`[[2]]
## $`T cell CD4+`[[2]]$grupo
## [1] "DLBCL with CNS inv" "DLBCL wo CNS inv"  
## 
## $`T cell CD4+`[[2]]$normalidad
## $`T cell CD4+`[[2]]$normalidad[[1]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.71347, p-value = 0.001283
## 
## 
## $`T cell CD4+`[[2]]$normalidad[[2]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.47814, p-value = 8.279e-07
## 
## 
## 
## $`T cell CD4+`[[2]]$homocedasticidad
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  2.8315 0.1088
##       19               
## 
## 
## 
## $`T cell CD8+`
## $`T cell CD8+`[[1]]
## $`T cell CD8+`[[1]]$grupo
## [1] "PCNSL" "SCNSL"
## 
## $`T cell CD8+`[[1]]$normalidad
## $`T cell CD8+`[[1]]$normalidad[[1]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.29913, p-value = 1.279e-08
## 
## 
## $`T cell CD8+`[[1]]$normalidad[[2]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.5803, p-value = 0.0003366
## 
## 
## 
## $`T cell CD8+`[[1]]$homocedasticidad
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value  Pr(>F)  
## group  1  3.0368 0.09536 .
##       22                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## $`T cell CD8+`[[2]]
## $`T cell CD8+`[[2]]$grupo
## [1] "DLBCL with CNS inv" "DLBCL wo CNS inv"  
## 
## $`T cell CD8+`[[2]]$normalidad
## $`T cell CD8+`[[2]]$normalidad[[1]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.36794, p-value = 1.064e-07
## 
## 
## $`T cell CD8+`[[2]]$normalidad[[2]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.67489, p-value = 0.0002231
## 
## 
## 
## $`T cell CD8+`[[2]]$homocedasticidad
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  1.1131 0.3046
##       19               
## 
## 
## 
## $`Endothelial cell`
## $`Endothelial cell`[[1]]
## $`Endothelial cell`[[1]]$grupo
## [1] "PCNSL" "SCNSL"
## 
## $`Endothelial cell`[[1]]$normalidad
## $`Endothelial cell`[[1]]$normalidad[[1]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.80586, p-value = 0.001392
## 
## 
## $`Endothelial cell`[[1]]$normalidad[[2]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.97759, p-value = 0.9213
## 
## 
## 
## $`Endothelial cell`[[1]]$homocedasticidad
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  1.1202 0.3014
##       22               
## 
## 
## $`Endothelial cell`[[2]]
## $`Endothelial cell`[[2]]$grupo
## [1] "DLBCL with CNS inv" "DLBCL wo CNS inv"  
## 
## $`Endothelial cell`[[2]]$normalidad
## $`Endothelial cell`[[2]]$normalidad[[1]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.87353, p-value = 0.1099
## 
## 
## $`Endothelial cell`[[2]]$normalidad[[2]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.79346, p-value = 0.007716
## 
## 
## 
## $`Endothelial cell`[[2]]$homocedasticidad
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.7693 0.3914
##       19               
## 
## 
## 
## $Macrophage
## $Macrophage[[1]]
## $Macrophage[[1]]$grupo
## [1] "PCNSL" "SCNSL"
## 
## $Macrophage[[1]]$normalidad
## $Macrophage[[1]]$normalidad[[1]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.7904, p-value = 0.0008317
## 
## 
## $Macrophage[[1]]$normalidad[[2]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.89627, p-value = 0.3896
## 
## 
## 
## $Macrophage[[1]]$homocedasticidad
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1   0.012 0.9139
##       22               
## 
## 
## $Macrophage[[2]]
## $Macrophage[[2]]$grupo
## [1] "DLBCL with CNS inv" "DLBCL wo CNS inv"  
## 
## $Macrophage[[2]]$normalidad
## $Macrophage[[2]]$normalidad[[1]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.81758, p-value = 0.02369
## 
## 
## $Macrophage[[2]]$normalidad[[2]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.94069, p-value = 0.5286
## 
## 
## 
## $Macrophage[[2]]$homocedasticidad
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  1.8073 0.1947
##       19               
## 
## 
## 
## $`NK cell`
## $`NK cell`[[1]]
## $`NK cell`[[1]]$grupo
## [1] "PCNSL" "SCNSL"
## 
## $`NK cell`[[1]]$normalidad
## $`NK cell`[[1]]$normalidad[[1]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.61832, p-value = 6.854e-06
## 
## 
## $`NK cell`[[1]]$normalidad[[2]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.59724, p-value = 0.0005754
## 
## 
## 
## $`NK cell`[[1]]$homocedasticidad
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.4982 0.4877
##       22               
## 
## 
## $`NK cell`[[2]]
## $`NK cell`[[2]]$grupo
## [1] "DLBCL with CNS inv" "DLBCL wo CNS inv"  
## 
## $`NK cell`[[2]]$normalidad
## $`NK cell`[[2]]$normalidad[[1]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.7541, p-value = 0.004003
## 
## 
## $`NK cell`[[2]]$normalidad[[2]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.7046, p-value = 0.000536
## 
## 
## 
## $`NK cell`[[2]]$homocedasticidad
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value  Pr(>F)  
## group  1  4.2543 0.05309 .
##       19                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## 
## $`uncharacterized cell`
## $`uncharacterized cell`[[1]]
## $`uncharacterized cell`[[1]]$grupo
## [1] "PCNSL" "SCNSL"
## 
## $`uncharacterized cell`[[1]]$normalidad
## $`uncharacterized cell`[[1]]$normalidad[[1]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.98307, p-value = 0.9719
## 
## 
## $`uncharacterized cell`[[1]]$normalidad[[2]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.87439, p-value = 0.2847
## 
## 
## 
## $`uncharacterized cell`[[1]]$homocedasticidad
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1   2.449 0.1319
##       22               
## 
## 
## $`uncharacterized cell`[[2]]
## $`uncharacterized cell`[[2]]$grupo
## [1] "DLBCL with CNS inv" "DLBCL wo CNS inv"  
## 
## $`uncharacterized cell`[[2]]$normalidad
## $`uncharacterized cell`[[2]]$normalidad[[1]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.8633, p-value = 0.08346
## 
## 
## $`uncharacterized cell`[[2]]$normalidad[[2]]
## 
##  Shapiro-Wilk normality test
## 
## data:  df_grupo$Fraccion[df_grupo$Group == g]
## W = 0.85612, p-value = 0.05128
## 
## 
## 
## $`uncharacterized cell`[[2]]$homocedasticidad
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.2035  0.657
##       19

Comparación entre Grupos

Debido a que no se cumplen los supuestos de normalidad y/o homocedasticidad para todos los grupos en todos los tipos celulares, se decide hacer contrastes tanto paramétricos como no paramétricos. Más concretamente, debido a que el objetivo es hacer dos comparaciones independientes, entre dos grupos experimentales, para todos los tipos celulares de todos los métodos de deconvolución, se decide realizar pruebas T-Test t.test (comparación de medias, paramétrico) y pruebas U de Mann-Whitney wilcox.test (comparación de medianas, no paramétrico).

## $`T cells CD8_PCNSL_vs_SCNSL`
## 
##  Wilcoxon rank sum exact test
## 
## data:  df[[col_name]] by df$Group
## W = 61, p-value = 0.367
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $`T cells follicular helper_PCNSL_vs_SCNSL`
## 
##  Wilcoxon rank sum exact test
## 
## data:  df[[col_name]] by df$Group
## W = 54, p-value = 0.6793
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $`T cells regulatory (Tregs)_PCNSL_vs_SCNSL`
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  df[[col_name]] by df$Group
## W = 52, p-value = 0.7234
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $`T cells gamma delta_PCNSL_vs_SCNSL`
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  df[[col_name]] by df$Group
## W = 59, p-value = 0.4219
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $Monocytes_PCNSL_vs_SCNSL
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  df[[col_name]] by df$Group
## W = 48, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $`Total B cells_PCNSL_vs_SCNSL`
## 
##  Wilcoxon rank sum exact test
## 
## data:  df[[col_name]] by df$Group
## W = 53, p-value = 0.7306
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $`Total Macrophages_PCNSL_vs_SCNSL`
## 
##  Wilcoxon rank sum exact test
## 
## data:  df[[col_name]] by df$Group
## W = 62, p-value = 0.3306
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $`Total NK_PCNSL_vs_SCNSL`
## 
##  Wilcoxon rank sum exact test
## 
## data:  df[[col_name]] by df$Group
## W = 48, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $`Total CD4 (non-regulatory)_PCNSL_vs_SCNSL`
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  df[[col_name]] by df$Group
## W = 55, p-value = 0.6144
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $Myeloid_PCNSL_vs_SCNSL
## 
##  Wilcoxon rank sum exact test
## 
## data:  df[[col_name]] by df$Group
## W = 22, p-value = 0.075
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $`T cells CD8_DLBCL_with_CNS_inv_vs_wo_CNS_inv`
## 
##  Wilcoxon rank sum exact test
## 
## data:  df[[col_name]] by df$Group
## W = 57, p-value = 0.9177
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $`T cells follicular helper_DLBCL_with_CNS_inv_vs_wo_CNS_inv`
## 
##  Wilcoxon rank sum exact test
## 
## data:  df[[col_name]] by df$Group
## W = 48, p-value = 0.6539
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $`T cells regulatory (Tregs)_DLBCL_with_CNS_inv_vs_wo_CNS_inv`
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  df[[col_name]] by df$Group
## W = 46, p-value = 0.5382
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $`T cells gamma delta_DLBCL_with_CNS_inv_vs_wo_CNS_inv`
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  df[[col_name]] by df$Group
## W = 80, p-value = 0.07239
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $Monocytes_DLBCL_with_CNS_inv_vs_wo_CNS_inv
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  df[[col_name]] by df$Group
## W = 36.5, p-value = 0.1601
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $`Total B cells_DLBCL_with_CNS_inv_vs_wo_CNS_inv`
## 
##  Wilcoxon rank sum exact test
## 
## data:  df[[col_name]] by df$Group
## W = 48, p-value = 0.6539
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $`Total Macrophages_DLBCL_with_CNS_inv_vs_wo_CNS_inv`
## 
##  Wilcoxon rank sum exact test
## 
## data:  df[[col_name]] by df$Group
## W = 50, p-value = 0.7564
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $`Total NK_DLBCL_with_CNS_inv_vs_wo_CNS_inv`
## 
##  Wilcoxon rank sum exact test
## 
## data:  df[[col_name]] by df$Group
## W = 50, p-value = 0.7564
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $`Total CD4 (non-regulatory)_DLBCL_with_CNS_inv_vs_wo_CNS_inv`
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  df[[col_name]] by df$Group
## W = 89, p-value = 0.01402
## alternative hypothesis: true location shift is not equal to 0
## 
## 
## $Myeloid_DLBCL_with_CNS_inv_vs_wo_CNS_inv
## 
##  Wilcoxon rank sum exact test
## 
## data:  df[[col_name]] by df$Group
## W = 61, p-value = 0.7045
## alternative hypothesis: true location shift is not equal to 0

Heatmaps

## [1] TRUE
## [1] TRUE
## [1] TRUE
## [1] TRUE

## [1] "14-B19-32346-A1" "14-B03-22923-B"  "14-B17-02714-10" "14-B20-22458-A1"
## [5] "5-B19-33807-A5"
## [1] "3-B21-18652"    "14-B13-29662-2" "3-B16-37292"    "3-B12-28752"   
## [5] "4-B06-00420"
## [1] "4-B06-00420"     "18-B23-15669-A1" "3-B10-35826"     "3-B16-39751"    
## [5] "3-B16-37292"

Correlación genes ~ tipos celulares

## [1] TRUE